Last updated on February 23, 2026 by Editorial Team
Author(s): Shahidullah Kausar
Originally published on Towards AI.
Machine Learning Interview Prep Part 28
Time-series forecasting is the process of predicting future values ​​based on historical data collected over time at regular intervals. It focuses on identifying patterns such as trends (long-term increase or decrease), seasonality (repeating patterns), and random fluctuations. Models such as ARIMA, exponential smoothing, and modern machine learning approaches learn from past behavior to predict future outcomes. Time-series forecasting is widely used in demand planning, financial markets, weather forecasting, energy consumption, and production forecasting, where understanding how values ​​evolve over time is more important than the relationships between independent variables.
The article discusses the fundamentals of time-series forecasting, detailing various models such as ARIMA and machine learning methods while emphasizing the importance of understanding underlying patterns such as trends and seasonality. It presents multiple choice questions to assess knowledge of these concepts, explaining the suitability of different forecasting models for specific scenarios, including the characteristics of stationary time series and the importance of variation and changes in meeting model assumptions.
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